

'''simple docstring'''
import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar
class 					_snake_case		(unittest.TestCase):






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Union[str, Any]						=  logging.get_logger()

				# the current default level is logging.WARNING
				UpperCAmelCase_     :      List[Any]						=  logging.get_verbosity()

				logging.set_verbosity_error()
				self.assertEqual(logger.getEffectiveLevel()			,logging.get_verbosity()     )

				logging.set_verbosity_warning()
				self.assertEqual(logger.getEffectiveLevel()			,logging.get_verbosity()     )

				logging.set_verbosity_info()
				self.assertEqual(logger.getEffectiveLevel()			,logging.get_verbosity()     )

				logging.set_verbosity_debug()
				self.assertEqual(logger.getEffectiveLevel()			,logging.get_verbosity()     )

				# restore to the original level
				logging.set_verbosity(SCREAMING_SNAKE_CASE_     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      List[Any]						=  logging.get_verbosity()

				UpperCAmelCase_     :      Optional[Any]						=  logging.get_logger("transformers.models.bart.tokenization_bart"     )
				UpperCAmelCase_     :      Tuple						=  "Testing 1, 2, 3"

				# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
				if level_origin <= logging.WARNING:
					with CaptureLogger(SCREAMING_SNAKE_CASE_     ) as cl:
						logger.warning(SCREAMING_SNAKE_CASE_     )
					self.assertEqual(cl.out			,msg + "\n"     )

				# this is setting the level for all of `transformers.*` loggers
				logging.set_verbosity_error()

				# should not be able to log warnings
				with CaptureLogger(SCREAMING_SNAKE_CASE_     ) as cl:
					logger.warning(SCREAMING_SNAKE_CASE_     )
				self.assertEqual(cl.out			,""     )

				# should be able to log warnings again
				logging.set_verbosity_warning()
				with CaptureLogger(SCREAMING_SNAKE_CASE_     ) as cl:
					logger.warning(SCREAMING_SNAKE_CASE_     )
				self.assertEqual(cl.out			,msg + "\n"     )

				# restore to the original level
				logging.set_verbosity(SCREAMING_SNAKE_CASE_     )






			@mockenv(TRANSFORMERS_VERBOSITY="error"     )
			def 		UpperCamelCase__							(  self     ):
				transformers.utils.logging._reset_library_root_logger()
				# this action activates the env var
				UpperCAmelCase_     :      Tuple						=  logging.get_logger("transformers.models.bart.tokenization_bart"     )

				UpperCAmelCase_     :      int						=  os.getenv("TRANSFORMERS_VERBOSITY"			,SCREAMING_SNAKE_CASE_     )
				UpperCAmelCase_     :      Optional[Any]						=  logging.log_levels[env_level_str]

				UpperCAmelCase_     :      Optional[Any]						=  logging.get_verbosity()
				self.assertEqual(
				    SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,f'''TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'''			,)

				# restore to the original level
				UpperCAmelCase_     :      str						=  ""
				transformers.utils.logging._reset_library_root_logger()






			@mockenv(TRANSFORMERS_VERBOSITY="super-error"     )
			def 		UpperCamelCase__							(  self     ):
				transformers.utils.logging._reset_library_root_logger()
				UpperCAmelCase_     :      int						=  logging.logging.getLogger()
				with CaptureLogger(SCREAMING_SNAKE_CASE_     ) as cl:
					# this action activates the env var
					logging.get_logger("transformers.models.bart.tokenization_bart"     )
				self.assertIn("Unknown option TRANSFORMERS_VERBOSITY=super-error"			,cl.out     )

				# no need to restore as nothing was changed






			def 		UpperCamelCase__							(  self     ):
				transformers.utils.logging._reset_library_root_logger()

				UpperCAmelCase_     :      Union[str, Any]						=  logging.get_logger("transformers.models.bart.tokenization_bart"     )
				UpperCAmelCase_     :      Optional[int]						=  "Testing 1, 2, 3"

				with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="1"     ):
					# nothing should be logged as env var disables this method
					with CaptureLogger(SCREAMING_SNAKE_CASE_     ) as cl:
						logger.warning_advice(SCREAMING_SNAKE_CASE_     )
					self.assertEqual(cl.out			,""     )

				with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""     ):
					# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
					with CaptureLogger(SCREAMING_SNAKE_CASE_     ) as cl:
						logger.warning_advice(SCREAMING_SNAKE_CASE_     )
					self.assertEqual(cl.out			,msg + "\n"     )




def  a__       (    )     ->					List[Any]:


	"""simple docstring"""





	disable_progress_bar()
	assert are_progress_bars_disabled()

	enable_progress_bar()
	assert not are_progress_bars_disabled()






"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 			lowercase					( lowercase__		):
			lowercase        =     ['''image_processor''', '''tokenizer''']
			lowercase        =     '''CLIPImageProcessor'''
			lowercase        =     ('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')






			def __init__(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	int=None      ,SCREAMING_SNAKE_CASE_  :	str=None      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  None
				if "feature_extractor" in kwargs:
					warnings.warn(
					    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
					    ''' instead.'''      ,SCREAMING_SNAKE_CASE_      ,)
					lowerCAmelCase												=  kwargs.pop('''feature_extractor'''			)

				lowerCAmelCase												=  image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
					raise ValueError('''You need to specify an `image_processor`.'''			)
				if tokenizer is None:
					raise ValueError('''You need to specify a `tokenizer`.'''			)

				super().__init__(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)






			def __call__(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	List[str]=None      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=None      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=None      ,**SCREAMING_SNAKE_CASE_  :	Any			)       ->	List[Any]:





				"""simple docstring"""


				if text is None and images is None:
					raise ValueError('''You have to specify either text or images. Both cannot be none.'''			)

				if text is not None:
					lowerCAmelCase												=  self.tokenizer(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)

				if images is not None:
					lowerCAmelCase												=  self.image_processor(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)

				if text is not None and images is not None:
					lowerCAmelCase												=  image_features.pixel_values
					return encoding
				elif text is not None:
					return encoding
				else:
					return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_			)      ,tensor_type=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	List[str]      ,*SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,**SCREAMING_SNAKE_CASE_  :	Tuple			)       ->	Optional[Any]:





				"""simple docstring"""


				return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]      ,*SCREAMING_SNAKE_CASE_  :	int      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	Any:





				"""simple docstring"""


				return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			@property
			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  self.tokenizer.model_input_names
				lowerCAmelCase												=  self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


